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May 17, 2026
Anima
Trained on Anima Base 1
Dataset with a mix of natural language and tag captions, unchanged from v3
Partitioned dataset and trained at multi-res 512, 768, 1024, 1280, 1536
Training config:
# trained using diffusion-pipe commit b0aa4f1e03169f3280c8518d37570a448420f8be
# NCCL_P2P_DISABLE="1" NCCL_IB_DISABLE="1" NCCL_CUMEM_ENABLE="0" deepspeed --num_gpus=1 train.py --deepspeed --config anima-lora.toml --i_know_what_i_am_doing
output_dir = '/mnt/d/anima/training_output/anima-base-1-light-v31'
dataset = 'dataset-anima-light.toml'
# training settings
epochs = 2
# Per-resolution batch sizes
micro_batch_size_per_gpu = [[512, 64], [768, 64], [1024, 32], [1280, 24], [1536, 16]]
pipeline_stages = 1
gradient_accumulation_steps = 1
gradient_clipping = 1
warmup_steps = 30
lr_scheduler = 'cosine'
# misc settings
save_every_n_epochs = 1
activation_checkpointing = true
partition_method = 'parameters'
save_dtype = 'bfloat16'
caching_batch_size = 1
map_num_proc = 8
steps_per_print = 1
compile = true
[model]
type = 'anima'
transformer_path = '/mnt/c/workspace/models/diffusion_models/anima-base-v1.0.safetensors'
vae_path = '/mnt/c/workspace/models/vae/qwen_image_vae.safetensors'
llm_path = '/mnt/c/workspace/models/text_encoders/qwen_3_06b_base.safetensors'
dtype = 'bfloat16'
#cache_text_embeddings = false
llm_adapter_lr = 8e-7
#timestep_sample_method = 'uniform'
flux_shift = true
multiscale_loss_weight = 0.5
sigmoid_scale = 1.3
[adapter]
type = 'lora'
rank = 32
dtype = 'bfloat16'
[optimizer]
type = 'adamw_optimi'
lr = 4e-5
betas = [0.9, 0.99]
weight_decay = 0.01
eps = 1e-8resolutions = [512, 768, 1024, 1280, 1536]
enable_ar_bucket = true
min_ar = 0.5
max_ar = 2.0
num_ar_buckets = 9
# 16 repeats from captions.json
# 1,321 steps 2.02 hours per epoch
# 866 total images
# images_light\1536x1536\captions.json with 270 entries.
[[directory]]
path = '/mnt/d/training_data/images_light/1536x1536'
resolutions = [512, 1024, 1280, 1536]
# images_light\1280x1280\captions.json with 59 entries.
[[directory]]
path = '/mnt/d/training_data/images_light/1280x1280'
resolutions = [512, 1024, 1280]
# images_light\1024x1024\captions.json with 368 entries.
[[directory]]
path = '/mnt/d/training_data/images_light/1024x1024'
resolutions = [512, 768, 1024]
# images_light\768x768\captions.json with 162 entries.
[[directory]]
path = '/mnt/d/training_data/images_light/768x768'
resolutions = [512, 768]
# images_light\512x512\captions.json with 7 entries.
[[directory]]
path = '/mnt/d/training_data/images_light/512x512'
resolutions = [512]
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License:
AnimaThe Anima Model is licensed by CircleStone Labs LLC. Copyright CircleStone Labs LLC. IN NO EVENT SHALL CIRCLESTONE LABS LLC BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH USE OF THIS MODEL.
Built on NVIDIA Cosmos
Light Concepts
Training data is a collection of various light concepts I enjoy using that are not overly represented in large datasets, trained as a single lora.
ℹ️ LoRA work best when applied to the base models on which they are trained. Please read the About This Version on the appropriate base models and workflow/training information.
I've trained many of these concepts before, generally they are nice to use to enhance the lighting of generations or give interesting effects.
Trained on a large mixed NL and tags dataset, at mixed [1024, 1536] resolutions. Previews are mostly generated at 1024x1536 with a combination of tags and NL prompts.
Concept tags
Not limited to, but collected by works containing:
dispersion
hue shifting
refraction
subsurface scattering
translucent
bioluminescence
caustics
dappled moonlight
glowing hot
ultraviolet lightWorks best in combination with NL if you name a character, describe their basic appearance, and finish with descriptions of light sources and their effects on the scene:
A vibrant and dynamic illustration of Hoshimachi Suisei from Hololive, featuring her squatting in front of a glowing triangular prism.
A beam of white light enters the prism from the left and refracts into a vibrant rainbow on the right. The background is a solid dark grey to emphasize the lighting effects.

